Physics-Informed Deep Neural Networks for Transient Electromagnetic Analysis

Article Properties
Journal Categories
Technology
Electrical engineering
Electronics
Nuclear engineering
Electric apparatus and materials
Electric circuits
Electric networks
Technology
Electrical engineering
Electronics
Nuclear engineering
Telecommunication
Refrences
Title Journal Journal Categories Citations Publication Date
Equations of motion from a data series 1987
Accelerating eulerian fluid simulation with convolutional networks 2017
Generating videos with scene dynamics 2016
Convolutional LSTM network: A machine learning approach for precipitation nowcasting 2015
Convolutional LSTM network: A machine learning approach for precipitation nowcasting 2016
Citations
Title Journal Journal Categories Citations Publication Date
Physics‐informed surrogates for electromagnetic dynamics using Transformers and graph neural networks

IET Microwaves, Antennas & Propagation
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Science: Physics: Electricity and magnetism
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
2024
Citations Analysis
The category Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Physics‐informed surrogates for electromagnetic dynamics using Transformers and graph neural networks and was published in 2024. The most recent citation comes from a 2024 study titled Physics‐informed surrogates for electromagnetic dynamics using Transformers and graph neural networks. This article reached its peak citation in 2024, with 1 citations. It has been cited in 1 different journals, 100% of which are open access. Among related journals, the IET Microwaves, Antennas & Propagation cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year